Course Material for COGNESTIC 2023

The Cognitive Neuroscience Skills Training In Cambridge (COGNESTIC) is a 2-week course that provides researchers with training in state-of-the-art methods for neuroimaging and neurostimulation. You can find more information on the COGNESTIC webpage.

Below you will find documents, videos and web links that will be used for the course or can be used for preparation.

The following materials will be updated nearer the event.

Introduction and Open Science Rik Henson & Olaf Hauk
Websites	OSF UKRN BIDS
Suggested reading	Munafo et al, 2017, problems in science Button et al, 2013, power in neuroscience Poldrack et al, 2017, reproducible neuroimaging Marek et al, 2022, power in neuroimaging association studies
Suggested viewing	Open Cognitive Neuroscience (will give this talk live on day) Statistical power in neuroimaging PayWall: open access Comedian's Perspective on science and media
Tutorial slides and scripts	Open Science Talk Slides

Structural MRI - VBM and Surface-based Analysis Marta Correia
Software	FSL Freesurfer
Datasets	Freesurfer tutorial data Subset of the CamCAN dataset (~3GB) https://www.cam-can.org/, please sign data user agreement if using
Suggested reading	Introduction to GLM for structural MRI analysis Good et al, 2001, A VBM study of ageing Smith et al, 2004, Structural MRI analysis in FSL Dale et al, 1999, Cortical surface-based analysis I Fischl et al, 1999, Cortical surface-based analysis II
Suggested viewing	Using the command line Introduction to MRI Physics and image contrast Slides
Tutorial slides and scripts	FSLVBM slides FSLVBM tutorial FSLVBM bash script FreeSurfer Cortical Thickness slides Freesurfer tutorials FS check location script FS visualising the output script FS group analysis script FS ROI analysis script

Diffusion MRI I - DTI Model Fitting and Group Analysis Marta Correia
Software	FSL
Datasets	BTC_preop
Suggested reading	FSL Diffusion Toolbox Wiki Le Bihan et al, 2015, What water tells us about biological tissues Soares et al, 2013, A short guide to Diffusion Tensor Imaging Smith et al, 2006, Tract-based spatial statistics (TBSS)
Suggested viewing	Introduction to Diffusion MRI - Part I Slides
Tutorial slides and scripts	FSL DTI and TBSS slides DTI and group analysis in TBSS tutorial FSL DTI pipeline script FSL TBSS script FSL Group QC script

Diffusion MRI II - Tractography and Structural Connectivity Marta Correia
Software	MRtrix3
Datasets	BTC_preop
Suggested reading	MRtrix3 documentation MR Diffusion Tractography
Suggested viewing	Introduction to Diffusion MRI - Part II Slides
Tutorial slides and scripts	MRtrix Tractography slides MRtrix Tractography tutorials MRtrix preprocessing script MRtrix CSD Tractography script MRtrix connectome script

fMRI I - Data management, structure, manipulation Dace Apšvalka
Software	HeudiConv, PyBIDS, NiBabel, Nilearn
Datasets	Wakeman Multimodal
Suggested reading	Gorgolewski et al., 2016, The brain imaging data structure (BIDS)
Suggested viewing	BIDS for MRI: Structure and Conversion by Taylor Salo (13:39) fMRI Data Structure & Terminology by Martin Lindquist and Tor Wager (6:47)
Slides and scripts	https://github.com/dcdace/COGNESTIC-fMRI

fMRI II - Quality control & Pre-processing Dace Apšvalka
Software	MRIQC, fMRIprep, Nipype
Datasets	Wakeman Multimodal
Suggested reading	Chen & Glover (2015), Functional Magnetic Resonance Imaging Methods Ashburner J & Friston KJ (2004), Rigid body registration Maclaren et al. (2013), Prospective Motion Correction in Brain Imaging: A Review Sladky et al. (2011), Slice-timing effects and their correction in functional MRI Friston et al. (2000), To Smooth or Not to Smooth?: Bias and Efficiency in fMRI Time-Series Analysis Esteban et al., 2018, fMRIPrep: a robust preprocessing pipeline for functional MRI
Suggested viewing	fMRI Artifacts and Noise by Martin Lindquist and Tor Wager (11:57) Pre-processing I by Martin Lindquist and Tor Wager (10:17) Pre-processing II by Martin Lindquist and Tor Wager (7:42)
Slides and scripts	https://github.com/dcdace/COGNESTIC-fMRI

fMRI III - Subject Level Analysis Dace Apšvalka
Software	Nipype, Nilearn, SPM12
Datasets	Wakeman Multimodal
Suggested reading	Friston et al. (1994), Statistical parametric maps in functional imaging: A general linear approach Poline & Brett (2012), Poline, J. B., & Brett, M. (2012). The general linear model and fMRI: does love last forever? Monti (2011), Statistical analysis of fMRI time-series: a critical review of the GLM approach Nichols & Hayasaka (2003), Controlling the familywise error rate in functional neuroimaging: a comparative review Chumbley & Friston (2009), False discovery rate revisited: FDR and topological inference using Gaussian random fields Woo et al. (2014), Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations Lindquist (2008), The Statistical Analysis of fMRI Data
Suggested viewing	The General Linear Model by Martin Lindquist and Tor Wager (12:24) GLM applied to fMRI by Martin Lindquist and Tor Wager (11:21) Model Building I – conditions and contrasts by Martin Lindquist and Tor Wager (11:48) Model Building II – temporal basis sets by Martin Lindquist and Tor Wager (11:08) Model Building III- nuisance variables by Martin Lindquist and Tor Wager (13:58) GLM Estimation by Martin Lindquist and Tor Wager (9:11) Noise Models- AR models by Martin Lindquist and Tor Wager (9:57) Inference- Contrasts and t-tests by Martin Lindquist and Tor Wager (11:05) Multiple Comparisons by Martin Lindquist and Tor Wager (9:03) FWER Correction by Martin Lindquist and Tor Wager (16:11) FDR Correction by Martin Lindquist and Tor Wager (5:25) More about multiple comparisons by Martin Lindquist and Tor Wager (14:39)
Slides and scripts	https://github.com/dcdace/COGNESTIC-fMRI

fMRI IV - Group Level Analysis & Reporting Dace Apšvalka
Software	Nilearn, PySurfer, SPM12
Datasets	Wakeman Multimodal
Suggested reading	Mumford & Nichols (2006), Modeling and inference of multisubject fMRI data Nichols et al. (2017), Best practices in data analysis and sharing in neuroimaging using MRI Poldrack et al. (2008), Guidelines for reporting an fMRI study Gorgolewski et al. (2016), NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain Markiewicz et al. (2021), The OpenNeuro resource for sharing of neuroscience data
Suggested viewing	Group-level Analysis I by Martin Lindquist and Tor Wager (7:05) Group-level Analysis II by Martin Lindquist and Tor Wager (10:09) Group-level Analysis III by Martin Lindquist and Tor Wager (14:01)
Slides and scripts	https://github.com/dcdace/COGNESTIC-fMRI

fMRI Connectivity Rik Henson& Petar Raykov
Software	SPM12
Datasets	Wakeman Multimodal
Suggested reading	Resting-state functional Connectivity Simple Intro to DCM fMRI preprocessing in SPM12 (for demo) SPM12 manual (Chapter 36)
Suggested viewing	fMRI Functional Connectivity, including DCM Bayesian Model Comparison (for DCM for fMRI/MEEG)
Tutorial slides and scripts	Tutorial for DCM for fMRI

EEG/MEG I – Pre-processing Olaf Hauk
Software	MNE-Python MNE Installation for Cognestic
Datasets	Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic
Suggested reading	Digitial Filtering Filtering How To Maxwell Filtering General EEG/MEG Literature
Suggested viewing	Introduction to EEG/MEG Preprocessing What are we measuring with M/EEG?
Slides and scripts	Notebooks and Slides

EEG/MEG II – Source Estimation Olaf Hauk
Software	MNE-Python MNE Installation for Cognestic
Datasets	Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic
Suggested reading	Linear source estimation and spatial resolution General EEG/MEG Literature
Suggested viewing	Introduction to EEG/MEG Source Estimation M/EEG Source Analysis in SPM
Slides and scripts	Notebooks and Slides

EEG/MEG III – Time-Frequency Analysis Olaf Hauk
Software	MNE-Python MNE Installation for Cognestic
Datasets	Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic
Suggested reading	Tutorial on Functional Connectivity Analyzing Neural Time Series Data General EEG/MEG Literature
Suggested viewing	Introduction to time-frequency and functional connectivity analysis Time-Frequency Analysis of EEG Time Series
Slides and scripts	Notebooks and Slides

EEG/MEG VI – Functional Connectivity and Multimodal Imaging Olaf Hauk
Software	MNE-Python MNE Installation for Cognestic
Datasets	Sample dataset in MNE-Python. Tutorials MNE Installation for Cognestic
Suggested reading
Suggested viewing
Slides and scripts

Brain Network Analysis Lena Dorschmidt
Software
Datasets
Suggested reading
Suggested viewing
Slides and scripts

MVPA/RSA I Daniel Mitchell
Software	The Decoding Toolbox and CoSMoMVPA using Octave.
Datasets	Example data from above toolboxes, plusWakeman Multimodal
Suggested reading	Mur et al. (2009) Revealing representational content with pattern-information fMRI--an introductory guide Hebart et al. (2014) The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave
Suggested viewing	Excellent presentations from Martin Hebart's MVPA course, on: Introduction to MVPA Introduction to classification. (I've suggested these two, but the others are worth a look too.)
Slides and scripts	Slides for morning session - MVPA This year’s demos will be based on Python & Octave; scripts will be updated in due course

MVPA/RSA II Daniel Mitchell
Software	Python implementation of the RSA Toolbox: Version 3.0
Datasets	Example data from toolbox above, plus Group-averged example data from Mitchell & Cusack (2016) Semantic and emotional content of imagined representations in human occipitotemporal cortex
Suggested reading	Kriegeskorte et al. (2008) Representational similarity analysis - connecting the branches of systems neuroscience Kriegeskorte & Kievit (2013) Representational geometry: integrating cognition, computation, and the brain Nili et al. (2014) A toolbox for representational similarity analysis EEG/MEG: Tutorial on EEG/MEG decoding Temporal Generalization
Suggested viewing	Martin Hebart's lecture on RSA
Slides and scripts	Slides for afternoon session - RSA EEGMEG Notebooks and Slides This year’s demos will be based on Python and Octave; scripts will be updated in due course

Brain Stimulation, Pethysmography, Electromyography Ajay Halai, Alexis Deighton McIntyre, Hristo Dimitrov
Software
Datasets	Brain Stimulation:
Suggested reading	Approaches to brain stimulation ; what can we infer from brain stimulation; using NIBS clinically ; focused ultrasound 1 and 2
Suggested viewing
Slides and scripts

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Course Material for COGNESTIC 2023